1. Field
Certain aspects of the present disclosure generally relate to neural system engineering and, more particularly, to systems and methods for modifying neural dynamics in a neural network model.
2. Background
An artificial neural network, which may comprise an interconnected group of artificial neurons (i.e., neuron models), is a computational device or represents a method to be performed by a computational device. Artificial neural networks may have corresponding structure and/or function in biological neural networks. However, artificial neural networks may provide innovative and useful computational techniques for certain applications in which traditional computational techniques are cumbersome, impractical, or inadequate. Because artificial neural networks can infer a function from observations, such networks are particularly useful in applications where the complexity of the task or data makes the design of the function by conventional techniques burdensome.
Researchers of spiking neural networks spend considerable time understanding and designing mathematical models of spiking neurons. These mathematical models can be arbitrarily complex and require manual tuning to produce some desired behavior. A designer will typically describe the neuron with a set of equations and parameters for those equations. Then, the parameters will be manipulated to match some characteristics of an existing or prototypical neuron. For example, the neuron model may be designed to reproduce the membrane voltage with respect to time of a neuron known to exist in biology. The researcher then uses the voltage over time of a known prototypical neuron as a reference and attempts to duplicate those dynamics with his own model. When presented with the same input as the prototypical neuron, the model neuron is meant to produce an accurate approximation of the membrane voltage after extensive tuning.